Pre-hospital emergency care system: Utilization of ambulance services in Nairobi county

Emergency Medical Service (EMS) is the system that organizes all aspects of medical care provided to patients in the pre-hospital environment and emergency department within hospitals. It comprises agencies and organizations (both private and public), communication and transportation networks, hospitals, highly trained professionals and a public aware on how to respond in emergencies. Kenya needs robust EMS due to the high prevalence of acute medical events such as COVID-19, natural and man-made disasters. The objective of this study was to assess utilization of ambulance services in Nairobi County. The study design was descriptive cross-sectional, using quantitative and qualitative research methods. Data was collected using interviewer-administered semi-structured questionnaires and key in-depth interviews from 14 ambulance services, 19 Emergency Care Centers (ECC), and community. Thirty-nine key informants were sampled using purposive sampling technique and 101 community members sampled using simple random sampling technique from 10 sub-counties. Data tools were pretested at Mukuru Kwa Njenga, a populous slum in Nairobi County. Approval to carry out the study was granted by the Ministry of Health, Kenya. Qualitative data was analyzed using NVIVO12 and quantitative data analyzed using descriptive statistics by use of SPSS vs 25 software. Findings are represented as frequencies and percentages. There were 14 ambulance service providers in the Nairobi Metropolitan Services area with a total of 42 ambulances. Forty-one ambulances were facility-based ambulances. All ambulance services had emergency numbers, and only 3 had short-toll free ambulance access numbers. There were 9 different ambulance dispatch centers in the county. Five ambulance services did not have a dispatch Centre. Public members were the first to assist in in 79% emergencies. There was low public awareness on available pre-hospital emergency care services and tollfree lines for emergency services. Ambulances utilization was also low.

Can ambulance dispatch categories discriminate traffic incidents that do/do not require a lights and sirens response?

PurposeTraffic incidents vary considerably in their severity, and the dispatch categories assigned during emergency ambulance calls aim to identify those incidents in greatest need of a lights and sirens (L&S) response. The purpose of this study was to determine whether dispatch categories could discriminate between those traffic incidents that do/do not require an L&S response.Design/methodology/approachA retrospective cohort study of ambulance records was conducted. The predictor variable was the Traffic/Transportation dispatch categories assigned by call-takers. The outcome variable was whether each incident required an L&S response. Possible thresholds for identifying dispatch categories that require an L&S response were developed. Sensitivity and specificity were calculated for each threshold.FindingsThere were 17,099 patients in 13,325 traffic incidents dispatched as Traffic/Transportation over the study period. “Possible death at scene” ‘had the highest odds (OR 22.07, 95% CI 1.06–461.46) and “no injuries” the lowest odds (OR 0.28 95% CI 0.14–0.58) of requiring an L&S response compared to the referent group. The area under the ROC curve was 0.65, 95% CI [0.64, 0.67]. It was found that Traffic/Transportation dispatch categories allocated during emergency ambulance calls had limited ability to discriminate those incidents that do/do not require an L&S response to the scene of a crash.Originality/valueThis research makes a unique contribution, as it considers traffic incidents not as a single entity but rather as a number of dispatch categories which has practical implications for those emergency medical services dispatching ambulances to the scene.

Establishing an ambulance dispatch system for intrahospital transfers in a large teaching hospital in India

During the Covid Pandemic, a lot of structural and process changes had to be made in a quick time in almost all the hospitals to accommodate the patients and admit them with the least exposure to the Hospital Staff and the bystanders of the patients. AIIMS Hospital in New Delhi India is a premier tertiary care teaching hospital, which is spread out in different areas. Two Hospital centers of AIIMS were designated as COVID Hospitals. Since there was no previous experience of intrahospital transfers of this magnitude, the hospital had to face lots of difficulties in such transfers and this translated into increased turnaround time. This paper concentrates on the mechanisms in which the Department of Hospital Administration found out the various issues plaguing this process. Later by Change Management, an Intervention was brought in, which helped in the framing of a standard operating procedure that helped in the easy transfer of the patients which was hassle-free and which continued to the second wave of the COVID pandemic.

Operating an eHealth System for Prehospital and Emergency Health Care Support in Light of Covid-19

Introduction: The support of prehospital and emergency call handling and the impact of Covid-19 is discussed throughout this study. The initial purpose was to create an electronic system (eEmergency system) in order to support, improve, and help the procedure of handling emergency calls. This system was expanded to facilitate needed operation changes for Covid-19.Materials and Methods: An effort to reform the procedures followed for emergency call handling and Ambulance dispatch started on the Island of Cyprus in 2016; along that direction, a central call centre was created. The electronic system presented in this work was designed for this call centre and the new organization of the ambulance services. The main features are the support for ambulance fleet handling, the support for emergency call evaluation and triage procedure, and the improvement of communication between the call centre and the ambulance vehicles. This system started regular operation at the end of 2018. One year later, when Covid-19 period started, we expanded it with the addition of several new features in order to support the handling of patients infected with the new virus.Results: This system has handled 112,414 cases during the last 25 months out of which 4,254 were Covid-19 cases. These cases include the transfer of patients from their house to the reference hospital, or the transfer of critical patients from the reference hospital to another hospital with an intensive care unit or transfer of patients from one hospital to another one for other reasons, like the number of admissions.Conclusion: The main purpose of this study was to create an electronic system (eEmergency system) in order to support, improve, and help the procedure of handling emergency calls. The main components and the architecture of this system are outlined in this paper. This system is being successfully used for 25 months and has been a useful tool from the beginning of the pandemic period of Covid-19.

Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response Time

Emergency medical services (EMS) provide crucial emergency assistance and ambulatory services. One key measurement of EMS’s quality of service is their ambulances’ response time (ART), which generally refers to the period between EMS notification and the moment an ambulance arrives on the scene. Due to many victims requiring care within adequate time (e.g., cardiac arrest), improving ARTs is vital. This paper proposes to predict ARTs using machine-learning (ML) techniques, which could be used as a decision-support system by EMS to allow a dynamic selection of ambulance dispatch centers. However, one well-known predictor of ART is the location of the emergency (e.g., if it is urban or rural areas), which is sensitive data because it can reveal who received care and for which reason. Thus, we considered the ‘input perturbation’ setting in the privacy-preserving ML literature, which allows EMS to sanitize each location data independently and, hence, ML models are trained only with sanitized data. In this paper, geo-indistinguishability was applied to sanitize each emergency location data, which is a state-of-the-art formal notion based on differential privacy. To validate our proposals, we used retrospective data of an EMS in France, namely Departmental Fire and Rescue Service of Doubs, and publicly available data (e.g., weather and traffic data). As shown in the results, the sanitization of location data and the perturbation of its associated features (e.g., city, distance) had no considerable impact on predicting ARTs. With these findings, EMSs may prefer using and/or sharing sanitized datasets to avoid possible data leakages, membership inference attacks, or data reconstructions, for example.

Analysis of the Interventions of Medical Emergency Teams in Older Patients in Selected Polish Cities with County Status: A Retrospective Cohort Study

Introduction: Geriatric patients account for a large proportion of interventions of medical emergency teams (METs). The aim of this study was to analyse medical emergency interventions in the Biała Podlaska and Chełm (Poland) between 2016 and 2018 in a group of patients ≥ 65 years of age. Materials and Methods: We analysed medical records of 1200 older patients treated by METs in Biała Podlaska and Chełm (Lublin Province, Poland). The research was conducted from June 2019 to March 2020 at the Emergency Medical Service Station in Biała Podlaska and the Medical Rescue Station in Chełm (Independent Public Complex of Health Care Facilities). Results: A total of 92.5% of medical emergency service interventions took place at the patient’s home. The mean time of stay at the scene was 20 min. The highest number of interventions occurred between 8:00 p.m. and 8:59 p.m. There were no statistically significant differences in the type of ambulance used depending on the patient’s sex, while there was a statistically significant relationship between priority code and sex. Cardiovascular diseases were diagnosed in 40% of patients, and the symptoms were not precisely classified in almost the same percentage of patients. Mortality cases accounted for 3.1% of the 1200 interventions analysed. Ambulance dispatch resulted in the patient being transported to the hospital emergency department in 69.1% of cases. Conclusions: METs were called for a variety of diseases due to the fact that geriatric patients are not able to distinguish a life-threatening condition. Medical procedures performed by METs from Biała Podlaska and Chełm were closely related to the initial diagnoses made by these teams. It was irrelevant whether a specialist or non-specialist medical emergency service was used. Paramedics are very well trained to practice their profession and are able to provide treatment to older patients in a state of sudden life threat.

High-level Emergency Care Centers and Hospitals with Both Medical/Surgical and Psychiatric Inpatient Beds are associated with a lower rate of difficulty in hospital acceptance and shorter prehospital transfer time for patients seeking care after self-harm behavior

Ambulance diversion has a significant impact on patient care outcomes. Self-harm behavior in particular is associated with difficulty in hospital acceptance. This is a retrospective observational study using the Japanese ambulance dispatch data in 2015. This study aims to determine if high-level emergency care centers and hospitals with both medical/surgical and psychiatric inpatient beds are associated with a decreased rate of difficulty in hospital acceptance and shorter prehospital transfer times for patients seeking medical care after self-harm behavior using 2015 Japanese ambulance dispatch database. Multivariable analyses were performed for difficulty in hospital acceptance and prehospital transfer time. Covariates such as age, gender, the population density, time, and severity, were included in the analysis, in addition to the presence of high-level emergency care centers and hospitals with both medical/surgical and psychiatric inpatient beds in the city. There was an association between decreased difficulty in hospital acceptance and the presence of high-level emergency care centers (Odds ratio 0.62, 95% Confidence Interval 0.55 to 0.70, P < 0.01) and hospitals with both medical/surgical and psychiatric inpatient beds (OR = 0.49, 95%CI, 0.37 to 0.65, P < 0.01). There was significant reduction in prehospital transfer time in the city with high-level emergency care centers (4.21 min, 95%CI 3.53 to 4.89, P < 0.01) and hospitals with medical/surgical and psychiatric inpatient beds (3.46 min, 95%CI 2.15 to 4.77, P < 0.01). High-level emergency care centers and hospitals with both medical/surgical and psychiatric inpatient beds were associated with significant decrease in difficulty in hospital acceptance and shorter prehospital transfer time. (The graphical abstract is shown in Fig. 1.)